Related Experiment Video
Updated: Oct 26, 2025
07:31
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
7.3K
Forecasting suicide rates in India: An empirical exposition
Prafulla Kumar Swain1, Manas Ranjan Tripathy2, Subhadra Priyadarshini1,3
1Department of Statistics, Utkal University, Bhubaneswar, Odisha, India.
Plos One
|July 29, 2021
Summary
India faces a rising suicide rate, with trends indicating continued increases for the next decade. Further research and public health investments are crucial for suicide prevention strategies.
Area of Science:
- Public Health
- Epidemiology
- Sociology
Background:
- Suicide is a significant public health and social crisis in India.
- National suicide statistics reveal a concerning upward trend, exceeding global averages.
- Limited historical analysis and forecasting exist for suicide in India.
Purpose of the Study:
- To analyze suicide rate trends and victim characteristics in India over 50 years.
- To forecast future suicide rates in India using time series analysis.
- To identify key factors and inform suicide prevention policies.
Main Methods:
- Longitudinal time series analysis of suicide data from 1969 to 2018 (National Crime Record Bureau Reports).
- Application of the Autoregressive Integrated Moving Average (ARIMA) (4,1,0) model for forecasting.
- Examination of suicide trends and characteristics of victims.
Main Results:
- A consistent and rising trend in suicide rates observed over the past five decades.
- Forecasts predict a continued increase in suicide cases in the near future, with a potential decline later.
- Illness-associated suicide is a significant concern, representing the second-highest number of cases.
Conclusions:
- No immediate substantial decrease in suicide deaths is projected for India.
- Enhanced data collection and analysis are vital for effective suicide prevention strategies.
- Investment in public healthcare, education, and employment generation is recommended to mitigate suicide rates.
Related Concept Videos
01:22Applications of Life Tables
147
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
147
01:18Introduction To Survival Analysis
451
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
451
01:20Actuarial Approach
163
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
163
01:18Survival Curves
398
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
398
01:14Parametric Survival Analysis: Weibull and Exponential Methods
729
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
729
01:24Kaplan-Meier Approach
326
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
326